Session Deliverable — All Tracks
What You Must Complete Today
One successful test run connected to your actual project domain, with a screenshot in your project log. The environment must be set up for your project — not a generic test.
Track A — Make / n8n: Project Scenario Setup
Track A — No-Code
1
Open your Make scenario or n8n workflow. Start a new scenario/workflow specifically for your project — not the test one from Week 4. Name it clearly.
2
Connect your data source. Add your first trigger or data input node. Connect it now and verify it returns data.
3
Add a placeholder for each action in your architecture. Do not wire logic yet — just add empty action nodes matching your architecture sketch.
4
Test trigger with live data. Run the trigger manually. Confirm real data flows through.
5
Screenshot and log. Screenshot the workflow canvas with nodes connected. Paste in your project log as environment setup proof.
Track B — OpenClaw / Hermes Agent: Project Agent Configuration
Track B — Low-Code
1
Create a project folder in VSCode named after your project.
2
Set a domain-specific system prompt. Edit your agent's system prompt to match your project domain. Be specific — it affects every response.
3
Install one relevant skill from ClawHub. Review the source code before installing. Connect and test via Telegram.
4
Create a .env file for API keys. Never commit this file to GitHub — add .env to .gitignore.
5
Send a domain-specific test message. Ask your agent something relevant to your project. Screenshot the conversation.
Track C — Agno: Project Agent Setup in VSCode
Track C — Full-Code
1
Create a new project folder in VSCode. This is where all code, configuration, and logs will live.
2
Create a virtual environment: python -m venv venv. Activate it. Install Agno: pip install agno.
3
Create a .env file with your ANTHROPIC_API_KEY. Add .env to .gitignore.
4
Write your project agent file. Start from the 10-line template from Lesson 4.3. Modify the model, tools, and instructions to match your project domain.
5
Run a domain-specific test prompt. Screenshot the terminal output showing the agent's response. Paste in your project log.
✏️ Student Reflection
After your environment session: What worked on the first try? What took longer than expected? Is there any part of your architecture that you now realise you cannot implement with your chosen track? If yes — flag it to your mentor before Week 6.📝 Your Lesson Submission
Share your reflection, ask a question, or leave a comment. Your mentor will respond.
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